Research of image segmentation based on 2-D maximum entropy optimal threshold

In order to improve the quality of image segmentation, two-dimensional maximum entropy optimal threshold (TDMEOT) method was used. Firstly, 2-D random vector of the domain pixels was defined through the gray region and TDMEOT value was gotten by the criterion function. Secondly, calculation data of 2-D maximum entropy threshold were optimized through the recursive optimization and the repetitive data calculation was reduced. Finally, based on the maximum mutual information criterion between the segmentation image area and the target space position and choosing error segmentation function as the segmentation standard, the algorithm flow and the image segmentation results of different algorithms were given after experimental simulation. The results show that this method has higher precision of image segmentation and has no residual background noise, and retains the image information with fast speed, good segmentation visual and minimum segmentation error. The research is helpful to improve the efficiency of image segmentation.